Analisa Sentiment Untuk Opini Alumni Perguruan Tinggi

Opinion is one of the most important parts in decision making, in processing opinions require a thorough analysis process. Especially text-based opinion, where opinion in the form of opinions do not have a definite value limit for the input. Sentiment Analysis as a branch of knowledge from Text mini...

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Main Authors: komang dharmendra, Komang Oka Saputra, I Nyoman Pramaita
Format: Article
Language:English
Published: Universitas Udayana 2019-07-01
Series:Majalah Ilmiah Teknologi Elektro
Online Access:https://ojs.unud.ac.id/index.php/JTE/article/view/48059
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spelling doaj-8bad2e81fa74486f9c308ca9aff2cb542020-11-25T03:29:10ZengUniversitas UdayanaMajalah Ilmiah Teknologi Elektro1693-29512503-23722019-07-01182xxxxxxxx10.24843/MITE.2019.v18i02.P1148059Analisa Sentiment Untuk Opini Alumni Perguruan Tinggikomang dharmendraKomang Oka SaputraI Nyoman PramaitaOpinion is one of the most important parts in decision making, in processing opinions require a thorough analysis process. Especially text-based opinion, where opinion in the form of opinions do not have a definite value limit for the input. Sentiment Analysis as a branch of knowledge from Text mining can be applied in the opinion analysis process in the form of text. Where opinions will be classified into 3 types of opinions, namely positive opinions, neutral opinions and negative opinions. This study grouped opinions from university graduated students using the SVM and NBC algorithms which in this study were divided into 3 main components, namely the input component, opinion grouping system, and output components.Opinion to be processed is data in the form of a * .csv format opinion file, which then conducts a grouping of opinions. Then the system produces output in the form of 3 types of opinions, namely, positive opinions, neutral opinions and negative opinions. In general, the accuracy results show the differences in the accuracy of each sentiment. From the test results generally shows the accuracy with the highest accuracy value in the NBC algorithm reaching 94.45 while the highest accuracy rate in the SVM algorithm reaches 75.76%.https://ojs.unud.ac.id/index.php/JTE/article/view/48059
collection DOAJ
language English
format Article
sources DOAJ
author komang dharmendra
Komang Oka Saputra
I Nyoman Pramaita
spellingShingle komang dharmendra
Komang Oka Saputra
I Nyoman Pramaita
Analisa Sentiment Untuk Opini Alumni Perguruan Tinggi
Majalah Ilmiah Teknologi Elektro
author_facet komang dharmendra
Komang Oka Saputra
I Nyoman Pramaita
author_sort komang dharmendra
title Analisa Sentiment Untuk Opini Alumni Perguruan Tinggi
title_short Analisa Sentiment Untuk Opini Alumni Perguruan Tinggi
title_full Analisa Sentiment Untuk Opini Alumni Perguruan Tinggi
title_fullStr Analisa Sentiment Untuk Opini Alumni Perguruan Tinggi
title_full_unstemmed Analisa Sentiment Untuk Opini Alumni Perguruan Tinggi
title_sort analisa sentiment untuk opini alumni perguruan tinggi
publisher Universitas Udayana
series Majalah Ilmiah Teknologi Elektro
issn 1693-2951
2503-2372
publishDate 2019-07-01
description Opinion is one of the most important parts in decision making, in processing opinions require a thorough analysis process. Especially text-based opinion, where opinion in the form of opinions do not have a definite value limit for the input. Sentiment Analysis as a branch of knowledge from Text mining can be applied in the opinion analysis process in the form of text. Where opinions will be classified into 3 types of opinions, namely positive opinions, neutral opinions and negative opinions. This study grouped opinions from university graduated students using the SVM and NBC algorithms which in this study were divided into 3 main components, namely the input component, opinion grouping system, and output components.Opinion to be processed is data in the form of a * .csv format opinion file, which then conducts a grouping of opinions. Then the system produces output in the form of 3 types of opinions, namely, positive opinions, neutral opinions and negative opinions. In general, the accuracy results show the differences in the accuracy of each sentiment. From the test results generally shows the accuracy with the highest accuracy value in the NBC algorithm reaching 94.45 while the highest accuracy rate in the SVM algorithm reaches 75.76%.
url https://ojs.unud.ac.id/index.php/JTE/article/view/48059
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AT komangokasaputra analisasentimentuntukopinialumniperguruantinggi
AT inyomanpramaita analisasentimentuntukopinialumniperguruantinggi
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